- Handling Missing Data
- Encoding Categorical Data ( LabelEncoder : Ordinal , OneHotEncoder : Nominal )
- Splitting the Dataset into Train Set and Test Set
- Feature Scaling ( MinMaxScaler : Normalization , StandardScaler : Standardization )
- Linear Regression
- Polynomial Regression
- Support Vector Regression ( SVR )
- Decision Tree Regression
- Random Forest Regression
- Evaluation of Predictive Models
- Hyperparameter Oprimization ( SVR, Decision Tree Regression and Random Forest Regression )
- Linear Regression, Polynomial Regression and Support Vector Regression requires Scaling for Better Accuracy and are Sensitive to Outliers
- Decision Tree and Random Forest does not need Scaling and are Less Prone to Outliers.
- fit_transform is only Applied on Training Data ( Learn the Parameter of Scaling and Scale the Data )
- Only transform is applied on Test Data ( The Scaling Parameter Learned by Training Data is Applied directly to Scale Test Data )
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit Learn : Preprocessing ( Min Max Scaler, Standard Scaler, Label Encoder, One Hot Encoder and Polynomial Features )
- Scikit Learn : Model Selection ( Train Test Split and Grid Search Cross Validation )
- Scikit Learn : SVM ( Support Vector Regressor : SVR )
- Scikit Learn : Tree ( Decision Tree Regressor )
- Scikit Learn : Ensemble ( Random Forest Regressor )
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